




'''simple docstring'''


import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest





class  __lowercase    (	__magic_name__			):
   _a    						=   (PNDMScheduler,)
   _a    						=   (("""num_inference_steps""", 50),)







   def 		UpperCamelCase__      (						self   ,				**UpperCamelCase   )					->     int:
     __a								=       {
         'num_train_timesteps': 1000,
         'beta_start': 0.0_001,
         'beta_end': 0.02,
         'beta_schedule': 'linear',
     }

     config.update(**UpperCamelCase   )
     return config







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase=0   ,				**UpperCamelCase   )					->     Tuple:
     __a								=       dict(self.forward_default_kwargs   )
     __a								=       kwargs.pop('num_inference_steps'   ,				UpperCamelCase   )
     __a								=       self.dummy_sample
     __a								=       0.1 * sample
     __a								=       [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

     for scheduler_class in self.scheduler_classes:
       __a								=       self.get_scheduler_config(**UpperCamelCase   )
       __a								=       scheduler_class(**UpperCamelCase   )
       scheduler.set_timesteps(UpperCamelCase   )
       # copy over dummy past residuals
       __a								=       dummy_past_residuals[:]

       with tempfile.TemporaryDirectory() as tmpdirname:
         scheduler.save_config(UpperCamelCase   )
         __a								=       scheduler_class.from_pretrained(UpperCamelCase   )
         new_scheduler.set_timesteps(UpperCamelCase   )
         # copy over dummy past residuals
         __a								=       dummy_past_residuals[:]

       __a								=       scheduler.step_prk(UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				**UpperCamelCase   ).prev_sample
       __a								=       new_scheduler.step_prk(UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				**UpperCamelCase   ).prev_sample

       assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"

       __a								=       scheduler.step_plms(UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				**UpperCamelCase   ).prev_sample
       __a								=       new_scheduler.step_plms(UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				**UpperCamelCase   ).prev_sample

       assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"







   def 		UpperCamelCase__      (						self   )					->     Optional[int]:
     pass







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase=0   ,				**UpperCamelCase   )					->     str:
     __a								=       dict(self.forward_default_kwargs   )
     __a								=       kwargs.pop('num_inference_steps'   ,				UpperCamelCase   )
     __a								=       self.dummy_sample
     __a								=       0.1 * sample
     __a								=       [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

     for scheduler_class in self.scheduler_classes:
       __a								=       self.get_scheduler_config()
       __a								=       scheduler_class(**UpperCamelCase   )
       scheduler.set_timesteps(UpperCamelCase   )

       # copy over dummy past residuals (must be after setting timesteps)
       __a								=       dummy_past_residuals[:]

       with tempfile.TemporaryDirectory() as tmpdirname:
         scheduler.save_config(UpperCamelCase   )
         __a								=       scheduler_class.from_pretrained(UpperCamelCase   )
         # copy over dummy past residuals
         new_scheduler.set_timesteps(UpperCamelCase   )

         # copy over dummy past residual (must be after setting timesteps)
         __a								=       dummy_past_residuals[:]

       __a								=       scheduler.step_prk(UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				**UpperCamelCase   ).prev_sample
       __a								=       new_scheduler.step_prk(UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				**UpperCamelCase   ).prev_sample

       assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"

       __a								=       scheduler.step_plms(UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				**UpperCamelCase   ).prev_sample
       __a								=       new_scheduler.step_plms(UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				**UpperCamelCase   ).prev_sample

       assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"







   def 		UpperCamelCase__      (						self   ,				**UpperCamelCase   )					->     Dict:
     __a								=       self.scheduler_classes[0]
     __a								=       self.get_scheduler_config(**UpperCamelCase   )
     __a								=       scheduler_class(**UpperCamelCase   )

     __a								=       10
     __a								=       self.dummy_model()
     __a								=       self.dummy_sample_deter
     scheduler.set_timesteps(UpperCamelCase   )

     for i, t in enumerate(scheduler.prk_timesteps   ):
       __a								=       model(UpperCamelCase   ,				UpperCamelCase   )
       __a								=       scheduler.step_prk(UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ).prev_sample

     for i, t in enumerate(scheduler.plms_timesteps   ):
       __a								=       model(UpperCamelCase   ,				UpperCamelCase   )
       __a								=       scheduler.step_plms(UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ).prev_sample

     return sample







   def 		UpperCamelCase__      (						self   )					->     Tuple:
     __a								=       dict(self.forward_default_kwargs   )

     __a								=       kwargs.pop('num_inference_steps'   ,				UpperCamelCase   )

     for scheduler_class in self.scheduler_classes:
       __a								=       self.get_scheduler_config()
       __a								=       scheduler_class(**UpperCamelCase   )

       __a								=       self.dummy_sample
       __a								=       0.1 * sample

       if num_inference_steps is not None and hasattr(UpperCamelCase   ,				'set_timesteps'   ):
         scheduler.set_timesteps(UpperCamelCase   )
       elif num_inference_steps is not None and not hasattr(UpperCamelCase   ,				'set_timesteps'   ):
         __a								=       num_inference_steps

       # copy over dummy past residuals (must be done after set_timesteps)
       __a								=       [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
       __a								=       dummy_past_residuals[:]

       __a								=       scheduler.step_prk(UpperCamelCase   ,				0   ,				UpperCamelCase   ,				**UpperCamelCase   ).prev_sample
       __a								=       scheduler.step_prk(UpperCamelCase   ,				1   ,				UpperCamelCase   ,				**UpperCamelCase   ).prev_sample

       self.assertEqual(output_a.shape   ,				sample.shape   )
       self.assertEqual(output_a.shape   ,				output_a.shape   )

       __a								=       scheduler.step_plms(UpperCamelCase   ,				0   ,				UpperCamelCase   ,				**UpperCamelCase   ).prev_sample
       __a								=       scheduler.step_plms(UpperCamelCase   ,				1   ,				UpperCamelCase   ,				**UpperCamelCase   ).prev_sample

       self.assertEqual(output_a.shape   ,				sample.shape   )
       self.assertEqual(output_a.shape   ,				output_a.shape   )







   def 		UpperCamelCase__      (						self   )					->     Union[str, Any]:
     for timesteps in [100, 1000]:
       self.check_over_configs(num_train_timesteps=UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Tuple:
     for steps_offset in [0, 1]:
       self.check_over_configs(steps_offset=UpperCamelCase   )

     __a								=       self.scheduler_classes[0]
     __a								=       self.get_scheduler_config(steps_offset=1   )
     __a								=       scheduler_class(**UpperCamelCase   )
     scheduler.set_timesteps(10   )
     assert torch.equal(
         scheduler.timesteps   ,				torch.LongTensor(
             [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]   )   ,				)







   def 		UpperCamelCase__      (						self   )					->     List[str]:
     for beta_start, beta_end in zip([0.0_001, 0.001]   ,				[0.002, 0.02]   ):
       self.check_over_configs(beta_start=UpperCamelCase   ,				beta_end=UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Optional[Any]:
     for schedule in ["linear", "squaredcos_cap_v2"]:
       self.check_over_configs(beta_schedule=UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     int:
     for prediction_type in ["epsilon", "v_prediction"]:
       self.check_over_configs(prediction_type=UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Optional[Any]:
     for t in [1, 5, 10]:
       self.check_over_forward(time_step=UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     str:
     for t, num_inference_steps in zip([1, 5, 10]   ,				[10, 50, 100]   ):
       self.check_over_forward(num_inference_steps=UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Dict:
     # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
     __a								=       27

     for scheduler_class in self.scheduler_classes:
       __a								=       self.dummy_sample
       __a								=       0.1 * sample

       __a								=       self.get_scheduler_config()
       __a								=       scheduler_class(**UpperCamelCase   )

       scheduler.set_timesteps(UpperCamelCase   )

       # before power of 3 fix, would error on first step, so we only need to do two
       for i, t in enumerate(scheduler.prk_timesteps[:2]   ):
         __a								=       scheduler.step_prk(UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ).prev_sample







   def 		UpperCamelCase__      (						self   )					->     Optional[Any]:
     with self.assertRaises(UpperCamelCase   ):
       __a								=       self.scheduler_classes[0]
       __a								=       self.get_scheduler_config()
       __a								=       scheduler_class(**UpperCamelCase   )

       scheduler.step_plms(self.dummy_sample   ,				1   ,				self.dummy_sample   ).prev_sample







   def 		UpperCamelCase__      (						self   )					->     List[Any]:
     __a								=       self.full_loop()
     __a								=       torch.sum(torch.abs(UpperCamelCase   )   )
     __a								=       torch.mean(torch.abs(UpperCamelCase   )   )

     assert abs(result_sum.item() - 198.1_318   ) < 1e-2
     assert abs(result_mean.item() - 0.2_580   ) < 1e-3







   def 		UpperCamelCase__      (						self   )					->     Dict:
     __a								=       self.full_loop(prediction_type='v_prediction'   )
     __a								=       torch.sum(torch.abs(UpperCamelCase   )   )
     __a								=       torch.mean(torch.abs(UpperCamelCase   )   )

     assert abs(result_sum.item() - 67.3_986   ) < 1e-2
     assert abs(result_mean.item() - 0.0_878   ) < 1e-3







   def 		UpperCamelCase__      (						self   )					->     List[str]:
     # We specify different beta, so that the first alpha is 0.99
     __a								=       self.full_loop(set_alpha_to_one=UpperCamelCase   ,				beta_start=0.01   )
     __a								=       torch.sum(torch.abs(UpperCamelCase   )   )
     __a								=       torch.mean(torch.abs(UpperCamelCase   )   )

     assert abs(result_sum.item() - 230.0_399   ) < 1e-2
     assert abs(result_mean.item() - 0.2_995   ) < 1e-3







   def 		UpperCamelCase__      (						self   )					->     List[str]:
     # We specify different beta, so that the first alpha is 0.99
     __a								=       self.full_loop(set_alpha_to_one=UpperCamelCase   ,				beta_start=0.01   )
     __a								=       torch.sum(torch.abs(UpperCamelCase   )   )
     __a								=       torch.mean(torch.abs(UpperCamelCase   )   )

     assert abs(result_sum.item() - 186.9_482   ) < 1e-2
     assert abs(result_mean.item() - 0.2_434   ) < 1e-3









'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_					=   {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   ["FNetTokenizer"]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   ["FNetTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "FNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "FNetForMaskedLM",
      "FNetForMultipleChoice",
      "FNetForNextSentencePrediction",
      "FNetForPreTraining",
      "FNetForQuestionAnswering",
      "FNetForSequenceClassification",
      "FNetForTokenClassification",
      "FNetLayer",
      "FNetModel",
      "FNetPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_fnet import FNetTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_fnet_fast import FNetTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_fnet import (
        FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        FNetForMaskedLM,
        FNetForMultipleChoice,
        FNetForNextSentencePrediction,
        FNetForPreTraining,
        FNetForQuestionAnswering,
        FNetForSequenceClassification,
        FNetForTokenClassification,
        FNetLayer,
        FNetModel,
        FNetPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase_					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



